SF-RAG: Structure-Fidelity Retrieval-Augmented Generation for Academic Question Answering

ArXi:2602.13647v2 Announce Type: replace-cross Efficient question-answering (QA) over extensive scientific literature is essential for evidence-based engineering decision-making. Retrieval-augmented generation (RAG) is increasingly applied to question-answering over long academic papers, where accurate evidence allocation under a fixed token budget is critical. However, existing approaches flatten papers into unstructured chunks, destroying the native hierarchical structure and forcing retrieval to operate in a disordered space.